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Journal of Archaeological Science 52 (2014) 410e420

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Journal of Archaeological Science

journal homepage: http: / /www.elsevier .com/locate/ jas

Combining glaciological and archaeological methods for gaugingglacial archaeological potential

Stephanie R. Rogers a, *, Mauro Fischer a, Matthias Huss a, b

a Department of Geosciences, Geography, University of Fribourg, Chemin du Mus�ee 4, 1700 Fribourg, Switzerlandb Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, 8093 Zurich, Switzerland

a r t i c l e i n f o

Article history:Received 30 April 2014Received in revised form8 September 2014Accepted 13 September 2014Available online 22 September 2014

Keywords:Glacial archaeologyGlaciologyGISLocational analysisLeast cost path analysisArchaeological prediction

* Corresponding author.E-mail address: stephrogers5@gmail.com (S.R. Rog

http://dx.doi.org/10.1016/j.jas.2014.09.0100305-4403/© 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

Recent climate changes have led to an increase in the exposure of archaeological remains in frozenenvironments due to the melting of glaciers and ice patches, and the thawing of permafrost. In somecases, the discovery of glacial archaeological findings has occurred due to chance. In order to avoid therisk of losing exceptional, often organic, cultural remains due to decomposition, systematic and pre-dictive methods should be employed to locate areas of high glacial archaeological potential. Here, wemerged archaeological and glaciological methods to create a new type of archaeological predictionmodel in the field of glacial archaeology. Locational analysis and glaciological modelling were used tohighlight current and future areas of archaeological potential in the Pennine Alps, located betweenSwitzerland and Italy. Future glacier area was calculated in 10 year increments until 2100. By 2090, 93% ofglacier area is expected to have disappeared. The results from the final model, GlaciArch, provide newinsights into future glacial archaeological prospection in the Pennine Alps by narrowing down a studyregion of 4500 km2 into several manageable square kilometre sites.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Due to the alternation of various warm and cold periods, glacierextents and ice volume storage have fluctuated in the entire Eu-ropean Alps during the Holocene (10.5 ka to present). Compared tothe Last Glacial Maximum (LGM) (19e20 ka BP) and latest Pleis-tocene, when large piedmont lobes of vast valley glaciers reachedthe Alpine foreland (Clark et al., 2009; Ivy-Ochs et al., 2008), glacierchanges have been rather minor during the Holocene. The glaci-erized area varied between the stage of the Little Ice Age (LIA)maximum, around 1850, and a minimum which was significantlysmaller than the present day extents (Grosjean et al., 2007;Holzhauser, 2007; Joerin et al., 2006, 2008).

Glacier-climate interactions have affected humans for millennia.In the European Alps, glacier fluctuations directly influenced hu-man interaction with Alpine areas (Benedict and Olson, 1978;Wiegandt and Lugon, 2008). For example, as glaciers receded af-ter the LGM, humans took advantage of the newly ice-free Alpinebiome which offered plenty of food and resources during thePaleolithic period (Pacher, 2003; Tagliacozzo and Fiore, 2000). The

ers).

present atmospheric warming has caused shrinkage of glaciers andice caps all over the world (IPCC, 2013). In consequence, melting iceand snow has uncovered archaeological remains in Arctic andAlpine environments (Andrews et al., 2012; Beattie et al., 2000;Callanan, 2012, 2013; Dixon et al., 2005; Farbregd, 1972; Farnellet al., 2004; Hafner, 2012; Hare et al., 2004, 2012; Lee, 2012;Rogers et al., 2014; VanderHoek et al., 2007) which further atteststo the use of frozen regions on a global scale. These artefacts whichhave melted out of ice patches and glaciers, and thawed out ofpermafrost, have created a new sub-discipline of archaeology:glacial archaeology. “Glacial archaeology” has also been referred toas ice patch archaeology (c.f. Andrews and MacKay, 2012; Reckin,2013) and frozen archaeology (Molyneaux and Reay, 2010).Perhaps one of the most famous examples of a glacial archaeolog-ical find is that of €Otzi the Tyrolean Iceman who was accidentallydiscovered by hikers in 1991 on the Italian/Austrian border, pro-truding from an ice patch (Prinoth-Fornwagner and Niklaus, 1994;Seidler et al., 1992). The uniqueness of €Otzi and other glacialarchaeological discoveries is that they have often been preserved byice for thousands of years, thus protecting them and providingscientists with unparalleled information about past cultures andclimates (Dixon et al., 2005; Reckin, 2013). There is urgency tocollect these delicate, often organic, glacial archaeological remainsbefore, or soon after, theymelt out of the ice and become destroyed

Delta:1_given nameDelta:1_surnameDelta:1_given namemailto:stephrogers5@gmail.comhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.jas.2014.09.010&domain=pdfwww.sciencedirect.com/science/journal/03054403http://www.elsevier.com/locate/jashttp://dx.doi.org/10.1016/j.jas.2014.09.010http://dx.doi.org/10.1016/j.jas.2014.09.010http://dx.doi.org/10.1016/j.jas.2014.09.010

S.R. Rogers et al. / Journal of Archaeological Science 52 (2014) 410e420 411

by decomposition (Andrews and MacKay, 2012; Dixon et al., 2005;Molyneaux and Reay, 2010). As melting in high altitudes and lati-tudes is not anticipated to halt in the near future (c.f. Radi�c et al.,2014), more glacial archaeological finds can be expected andthere is a need to further develop predictive methods in thisresearch domain.

In this paper, archaeological and glaciological methods aremerged together to create a new type of predictive model todetermine areas of glacial archaeological potential. The results willbe used as a decision support tool for future prospection ofarchaeological findings in high mountain environments. Ourapproach, referred to as “GlaciArch” in the following, is based oncurrent ice thickness distribution, future evolution of glacierizedareas, and topographic characteristics of the terrain which couldhave influenced past human accessibility. First, currently glaci-erized or recently deglacierized high altitude mountain passeslocated on the border of Switzerland and Italy are selected to beused as sites onwhich to perform the locational analysis. Next, leastcost paths (LCPs) are calculated between valleys and respectivepasses. Then, locational analysis is used to determine areas ofglacial archaeological potential based on the physical characteris-tics of the terrain. After, the future evolution of glaciers is modelledfor the Pennine Alps using a glacier evolution model (Huss et al.,2010a). Finally, the results of glacier modelling are combinedwith the results of locational analysis to create GlaciArch, a pre-dictive model which ultimately defines regions of highest

Fig. 1. Overview of study area. Glacieri

archaeological interest for now and the future. This paper high-lights how the intersection of glaciological and archaeologicalmethods provides a new approach for looking at glacial archaeo-logical prospection.

2. Study area and data

2.1. Study area

The Pennine Alps (centered at approximately 45�570N, 7�320E)are located between the canton of Valais, Switzerland, and theprovinces of Aosta and Piedmont, Italy (Fig. 1). The whole region isof particular glacial archaeological interest due to its large glaci-erized area and rich cultural heritage. The Pennine Alps coverapproximately 4500 km2 and reach altitudes above 4000 m a.s.l.The main valleys to the north, south, and east of the Pennine Alps,the Rhone valley (Switzerland), and the Aosta and Antigorio valleys(Italy) respectively, are scattered with archaeological remainsdating fromMesolithic (9.5 ka to5.5 ka BC) to historic times (Curdy,2007; Radmilli, 1963). Although most travellers reached thesevalleys from lower altitudes, each valley could also be reached bycrossing the Pennine Alps between them. This relatively shortdistance was often traversed for commercial purposes. Archaeo-logical remains collected on the way to, and on top of, mountainpasses between Switzerland and Italy demonstrate the use of thesepasses as trade and travel routes for thousands of years (Bezinge

zed areas are shaded in dark grey.

S.R. Rogers et al. / Journal of Archaeological Science 52 (2014) 410e420412

and Curdy, 1994, 1995; Coolidge, 1912; Curdy, 2007; Curdy et al.,2003; Harriss, 1970, 1971; Lehner and Julen, 1991; Rogers et al.,2014). In fact, early Neolithic culture seems to have spread toValais via the high altitude passes of the Pennine Alps from thesouth, possibly due to the grazing of small herds in the high pas-tures in summer (Curdy et al., 2003; Curdy, 2007). ThroughoutPrehistory and the Roman Period, therewere indications of a strongcultural relationship between Aosta and the Rhone valley; later, theAosta and Upper Rhone valleys were integrated as the uniqueecclesiastical province of Tarentasia for several centuries (Harriss,1970; Curdy, 2010). The relatively few archaeological remainsfound at high altitudes in this region should not be considered to bea direct result of the use of these high altitude passes. In the past,contrary to current beliefs, high altitude regions were used moreoften than assumed, and proved to be more hospitable than theyseem to modern day people (Aldenderfer, 2006; Reckin, 2013;Walsh et al., 2006).

2.2. Data

The high altitude pass names and locations used in the first stepof the locational analysis are derived from the 25 m resolutionSwissNames database provided by the Swiss Federal Office ofTopography (swisstopo) (Federal Office of Topography (2014))which contains all names given on the 1:25,000 national topo-graphic maps. The 1973 Swiss glacier inventory (Müller et al., 1976)was used to determine glacierized or recently deglacierized passes.The Digital Elevation Model (DEM) used in both the Least Cost PathAnalysis (LCPA) and slope calculation was the global 30 m resolu-tion Advanced Spaceborne Thermal Emission and Reflection Radi-ometer Global Digital Elevation Model (ASTER GDEM) (version 2)(NASA, 2012). Although more accurate DEMs exist, it was notpossible to obtain a consistent DEM for each side of the PennineAlps. The ASTER GDEM provides a consistent and sufficient dataaccuracy for these regional scale calculations in this study.

For the Swiss glaciers, the new Swiss Glacier Inventory SGI2010(Fischer et al., in press) was used. This layer was created by manualdigitization from high resolution (50 cm) aerial orthoimagery ac-quired between 2008 and 2011. For the Italian glaciers, outlinesbased on satellite imagery of 2003 were used (Paul et al., 2011).Surface topography for each glacier was extracted by intersectingglacier outlines with terrain elevation data. Glacier ice thicknessdistribution and bedrock topography were calculated based on aflux-gate approach and using the principles of ice flow dynamics(Huss and Farinotti, 2012). Information on surfacemass balance of alarge sample of glaciers in the Pennine Alps over the last decades isavailable from a combination of direct field observations, geodeticice volume changes and distributed modelling (Huss, 2012). Sce-narios for the future evolution of climatological variables are ob-tained from Regional Climate Models (RCM) from the CH2014project (CH2014-Impacts, 2014).

3. Background

In the Swiss Alps, glacial archaeological finds have been locatedat four locations: two sites on the border of the cantons of Valaisand Bern in the Bernese Alps, the L€otschenpass (Bellwald, 1992;Meyer, 1992) and Schnidejoch pass (Hafner, 2012); one site ineastern Switzerland, the Porchabella glacier (Rageth, 1995); and inValais, located in the Pennine Alps, near the Theodulpass (Lehnerand Julen, 1991; Meyer, 1992). The oldest and most notable findswere discovered between 2004 and 2011 at the Schnidejoch passfrom a melting ice patch which was formerly attached to theChilchli glacier on the north side of the pass. The ages of the findsrange from the Neolithic, Early Bronze Age, Iron Age, Roman, and

Medieval periods making this one of the most prolific glacialarchaeological sites in the Alps (Hafner, 2012). The abundance offinds can be attributed to the location of the ice patch which is in asmall depression facing northeast where ice has accumulated overcenturies. The Pennine Alps' most prolific glacial archaeological siteto date has been that of the Theodulpass. The finds, which includeskeletal remains, leather clothing and shoe soles, weapons, andcoins from the “Mercenary of Theodul”, date back to the 16thcentury (Lehner and Julen, 1991; Meyer, 1992). These items werefound between 1985 and 1990 along the margins of the ObererTheodul glacier on the Swiss side of the border. It is believed thatthe Mercenary fell into a crevasse and was preserved for hundredsof years until glacial dynamics andmelting eventually released himand his belongings.

3.1. Archaeological predictive modelling

In archaeology, the use of predictive modelling began in the1980's and has since grown into awide research field, mostly due toan increase in the accessibility to Geographic Information Systems(GIS) software and the ever-improving spatial resolution of data(c.f. Ebert, 2004; Kvamme, 1999; McCoy and Ladefoged, 2009). Inmost cases, archaeological predictive modelling is used to forecastthe location of archaeological sites based on the presence orabsence of defined criteria, in order to allocate information aboutknown patterns onto unknown places (Conolly and Lake, 2006;Warren and Asch, 2003; Wheatley and Gillings, 2002). Inputs topredictive models usually include sampled sites and have the maingoal of finding new sites which were used for human occupation(Carleton et al., 2012; Carrer, 2013; Graves, 2011; Kohler and Parker,1986). Put simplistically, predictive methods have been used todetermine the level of archaeological potential in a region andprovide decision-makers with a tool to justify why certain areas aremore archaeologically interesting than others (McCoy andLadefoged, 2009).

In glacial archaeology specifically, predictivemethods have beenused in relatively few instances but show promising results(Andrews et al., 2012; Dixon et al., 2005). For example, in Alaska,Dixon et al. (2005) were the first to use predictive modelling forglacial archaeological purposes by using aweighted combination ofcultural, biological, and geological input layers to successfullydetermine areas of high glacial archaeological potential. Similarly,Andrews et al. (2012) used remotely sensed data andotherweightedinput layers to determine areas of glacial archaeological potential innorthern Canada. Both previous studies were conducted at variousice patch sites. This study, which focuses on the potential of locatingglacial archaeological remains on or near glaciers, differs from pre-vious ones basedon thedistinctive environments. Ice characteristicsand glacier dynamics can strongly affect the potential of locatingglacial archaeological remains, and thus should be researched ac-cording to local conditions. For example, glaciers composed of thickice or located on steep slopes move relatively quickly and woulddestroy anything entrained within it in a matter of a few hundredyears basedon theprinciples of ice dynamics (BennandEvans, 2010;Dixon et al., 2005; Hafner, 2012). The margins of slower-movingglaciers could prove to be a better environment for finding glacialarchaeological remains, with the ideal environment being sur-rounded by ice with little or no movement such as ice patches (likethe Schnidejoch site) or slow-moving small glaciers located onrelatively flat terrains (like the Theodul site).

3.2. Least cost path analysis

LCPA is one type of archaeological prediction method used inGIS to calculate the “optimal” path across a landscape based on one

Table 1Names and locations of high altitude passes on the border between Switzerland andItaly which were glacierized in 1973.

Number Name Latitude (N) Longitude (E) Altitude (m)

1 Petit Col Ferret 45� 53' 58" 7� 4' 9" 24902 Col d'Amiante 45� 55' 6" 7� 18' 9" 33193 Col de la Balme 45� 54' 7" 7� 22' 27" 33214 Col du Petit Mont Collon 45� 57' 42" 7� 29' 11" 32925 Col Collon 45� 57' 41" 7� 30' 51" 30876 Col des Bouquetins 45� 59' 13" 7� 33' 36" 33577 Col de la Tête Blanche 45� 59' 33" 7� 34' 53" 35798 Tiefmattenjoch 45� 58' 22" 7� 35' 13" 35439 Breuiljoch 45� 58' 18" 7� 40' 18" 331310 Theodulpass 45� 56' 38" 7� 42' 35" 330111 Passo di Ventina Nord 45� 56' 4" 7� 42' 39" 345012 Breithornpass (south) 46� 14' 36" 8� 5' 12" 336813 Zwillingsjoch 45� 55' 36" 7� 47' 24" 384514 Felikjoch 45� 55' 5" 7� 48' 11" 406615 Lisjoch 45� 55' 18" 7� 51' 12" 416916 Neues Weisstor 45� 59' 20" 7� 54' 4" 350917 Seewjinenlücke 45� 59' 53" 7� 57' 22" 309518 Tossenjoch 46� 7' 31" 8� 3' 22" 292319 Breithornpass (east) 45� 55' 56" 7� 44' 34" 3845

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or more predefined input criteria (Anderson and Gillam, 2000; Belland Lock, 2000; Egeland et al., 2010; Gorenflo and Gale, 1990;Howey, 2007; Madry and Rakos, 1996; Rogers et al., 2014;Verhagen and Jeneson, 2012). It allows archaeologists to gain abetter understanding about movement patterns in prehistoric orhistoric terrains (Llobera et al., 2011; Murrieta-Flores, 2010, 2012;White and Surface-Evans, 2012). It is based on the principal thathumans will take the easiest path from one location to another ifthere are no other social or cultural forces directing them other-wise. The concept is not unique to archaeology and was developedfirstly in psychology and has since been used in various researchfields (Zipf, 1949).

3.3. Locational analysis

Locational analysis, also referred to as archaeological locationmodelling (ALM) or site predictive modelling, is a predictivemethod which calculates archaeological potential based on multi-ple weighted inputs, often including known archaeological sitelocations (Andrews et al., 2012; Carleton et al., 2012; Carrer, 2013;Dixon et al., 2005; Egeland et al., 2010). Like the term predictivemodelling, locational analysis has various meanings and a defini-tion which has developed over time (Kvamme, 1999; McCoy andLadefoged, 2009). Therefore, we define locational analysis usingMcCoy and Ladefoged's (2009) descriptionwhereby the potential ofundiscovered sites will be determined by calculating zones offuture prospection without spatially analysing known site loca-tions. This method is often used in vast areas from which archae-ological remains are sparse, possibly due to a lack of prospection.

3.4. Glacier retreat modelling

The atmospheric warming observed during the last decades hascaused a considerable reduction in area andmass of glaciers and icecaps all around the globe (Zemp et al., 2009). As an immediateresponse, changes in the climatic forcing acting on glaciers lead tochanges in the surface mass balance, that is, to changes in thequantity of snow and ice added to or melted from the glacier. Theeffects of the observed glacier changes are numerous and apply to abroad range of spatio-temporal scales, from global sea level rise(Gardner et al., 2013) to regional impacts on runoff in major rivercatchments (Kaser et al., 2010), to local consequences for landscapeevolution, hydropower production, natural hazards and tourism(Cannone et al., 2008; Farinotti et al., 2012; Fischer et al., 2011).Consequently, the projection of future glacier evolution has gainedincreasing attention in glaciology.

In recent years, various glacier modelling approaches have beendeveloped. For individual glaciers, they range from simple 2Dflowline models (Oerlemans et al., 1998; Van de Wal and Wild,2001) to complex 3D coupled mass-balance ice-flow models(Jouvet et al., 2011; Schneeberger et al., 2003). To calculate glacierresponse at the regional scale, simpler approaches neglectingtransient changes and the effect of ice dynamics were used (Paulet al., 2007; Schaefli et al., 2005). More advanced models drivenby distributed surface mass balance input and employing aparameterization of glacier ice flow have also been applied both atthe single-glacier scale and to the entire European Alps (Huss,2012; Huss et al., 2010a; Salzmann et al., 2012).

4. Methods

GlaciArch is composed of various steps which culminate in thecreation of a predictive model which gauges the glacial archaeo-logical potential of the Pennine Alps. The respective steps arepresented in the next sections.

4.1. High altitude pass selection and LCPs

The first step was to determine which high altitude passes onthe Swiss/Italian border were glacierized in 1973. This coincideswith the notion that recently deglacierized passes have a higherglacial archaeological potential and should be prospected firstbased on the fragility of glacial archaeological remains and arte-facts. The 1973 glacier inventory (Müller et al., 1976) was chosenbecause it covers an ideal time frame as archaeological itemsexposed over the last 40 years might have a better chance to persistcompared to 160 years if using the glacier inventory from orthoi-magery prior to the SGI2010 (Fischer et al., 2014). The passes werechosen using the selection tools in ArcGIS 10.1. First, the select byattributes tool was used to query all mountain passes which couldbe crossed by either foot or by road from the SwissNames databasefor the canton of Valais, resulting in the selection of 670 records.Next, the select by location tool was used to query those passeswhich were glacierized in 1973 from the currently selected records,to highlight the ones that were recently deglacierized, or stillcurrently glacierized, leaving 111 passes. From those 111 passes, theones on and near the border between Switzerland and Italy wereselected as it is known that some of the high altitude passes in thisstudy region have been used for thousands of years. This resulted inthe final extraction of 19 border passes which were glacierized in1973 from which to calculate LCPs (Table 1, Fig. 2a).

LCPs were calculated using the method described by Rogerset al. (2014) from each of the 19 passes to their nearest respectivemain valleys (Rhone, Aosta, or Antigorio) (Fig. 2b). This methodused Tobler's (1993) hiking algorithm to calculate walking timesbased on slope and prehistoric landcover as inputs (Bell and Lock,2000; Gorenflo and Gale, 1990; Rogers et al., 2014; Tobler, 1993;Verhagen and Jeneson, 2012; Whitley and Hicks, 2003). The pathdistance and cost distance tools in ArcGIS, which calculate theaccumulative cost across the terrain from a starting location andthe shortest path from a destination back to starting location,respectively, were used for the calculations.

4.2. Locational analysis

In this part of the analysis, multiple criteria were used to locateareas of high archaeological potential by analysing where peoplewere able to travel based on the topographic characteristics of theterrain by measuring the distance from LCPs and the slope of the

Fig. 2. Visualization of the locational analysis processing steps: (a) calculation of least cost paths with pass number corresponding to Table 1, (b) calculation of buffers around eachpath, (c) weighted slope values derived from DEM, (d) weighted glacier thickness layer, (e) selected close-up of slope multiplied by thickness, and (f) the final weighted locationalanalysis layer with pass numbers (see Table 1).

Table 2Weight values for the layers used in the locational analysis.

Weights Distance from LCPs (m) Slope (�) Ice thickness (m)

5 0e100 0e10 0e254 100e250 10e20 25e503 250e500 20e30 50e752 500e750 30e40 75e1001 750e1000 >40 >100

S.R. Rogers et al. / Journal of Archaeological Science 52 (2014) 410e420414

terrain, and where archaeological remains might be located basedon glacial characteristics and ice thickness. The input layers aredescribed in the following paragraphs.

Buffers were constructed around each path in 100, 250, 500,750, and 1000 m intervals on each side of the path to represent thenotion that archaeological potential decreases with distance fromthe paths. The zones within the 100 m buffers were assumed tohave the highest archaeological potential and weighted with thevalue of 5, while the zones located within the 1 km buffer wereassumed to have to lowest potential andweightedwith the value of1 (Table 2). The values in between are listed in Table 2.

The 1000 m buffers calculated above were used to define thestudy area surrounding the paths from which to calculate slopevalues. The extract by mask tool was used to isolate the study area

for the DEM and the slope tool was used to calculate the steepnessof the terrain (Fig. 2c). The slope values were calculated andweighted based on the potential of finding archaeological remains.Slopes greater than 40� were given a value of 1, thus very low

S.R. Rogers et al. / Journal of Archaeological Science 52 (2014) 410e420 415

archaeological potential, as they are difficult to climb and wouldprobably have been avoided. Furthermore, at steep slopes archae-ological remains are more likely to be washed out by erosion.Slopes between 0 and 10� were determined to be the easiest towalk across and thus were given the highest potential value of 5.Slopes between 10 and 40� were assigned with a respective linearlyincreasing potential value for 10� classes (Table 2).

Next, the glacier ice thickness layer was weighted from low tohigh archaeological potential. The ice thickness range between0 and 25 m was assigned a weight of 5 as those are the areas withthe highest potential now and in future years because they willlikely be the first to become ice-free (Table 2). Even more impor-tantly, archeological remains are only likely to be preserved belowrelatively thin ice. Thicker ice tends to flow faster and is generallymore destructive in terms of bedrock erosion. The classes with25e50 m, 50e75 m, and 75e100 mwere weighted with 4, 3, and 2,respectively (Fig. 2d).

The weighted layers for distance from LCPs, slope, and thicknesswere multiplied together to obtain one layer containing all possiblevalue combinations. The results ranged from 2 to 125 in 29 differentclasses (Fig. 2e). As afinal step in the locational analysis, these classeswere combined into five final potential categories using the NaturalBreaks classification scheme which arranges classes into “natural”objectively selected categories (Fig. 2f) (Jenks and Caspall, 1971).

4.3. Glaciological modelling

Future changes in glacier coverage over the entire Pennine Alpswere assessed by a combination of different glaciological models athigh spatial resolution. First, the current glacier ice thickness dis-tribution was derived using the glacier outlines from Fischer et al.(2014) for Switzerland and from Paul et al. (2011) for Italy basedon the approach by Huss and Farinotti (2012). Next, surface massbalance and 3D glacier geometry changewere modelled transientlyfor 50 Swiss glaciers from 2010 to 2100 based on a detailed glaciermodel (Huss et al., 2010a). The model runs at daily resolution on a25 m grid and takes into account snow accumulation distribution,the influence of radiation on ice melting, and calculated glacierretreat based on a mass-conservation approach. Model calibrationand validation for the 50 investigated glaciers was achieved with avariety of field data covering the entire 20th century (Huss et al.,2010b). For calculating future glacier change, we chose to use onesingle regional climate scenario for simplicity although the pro-jected evolution of meteorological variables is subject considerableuncertainties. Seasonal changes in air temperature and precipita-tion as projected by the Eidgen€ossische Technische HochschuleZürich (ETHZ, Swiss Federal Institute of Technology) RCM wereused as inputs into themodel. This climatemodel was driven by theA1B CO2-emission scenario (Nakicenovic, 2000). Until 2100, a meanannual air temperature rise of þ4.7 �C relative to 1980e2009 isexpected for the study region and precipitation is found to increasein winter but to decrease in summer (CH2014-Impacts, 2014).Finally, we extrapolated annual mass balance from the 50 glaciersto every glacier in the Pennine Alps (Huss, 2012). Thus, for eachglacier, a glacier-specific transient annual series of the glacier massbudget was obtained which was used to drive the glacier retreatmodel (Huss et al., 2010a). From the transient model runs, weextracted glacier ice coverage for 10-year time steps between 2020and 2100. These glacier masks were overlaid onto the results of thelocational analysis.

4.4. GlaciArch

In this step, past (1850 and 1973), current (2010), and selectedfuture (2030, 2060, and 2090) glacier extents were overlaid onto

locational analysis results to create the GlaciArch predictive model.The current archaeological potential of a region was assesseddifferently than that of the future potential. Current archaeologicalpotential is considered to exist in the regions that have beendeglacierized since 1973; that is, between the 1973 and 2010 ex-tents. Those are the general areas where archaeological remainscould be currently located based on the principles of glacier dy-namics. Future archaeological potential is ultimately gauged bycomparing the modelled glacier extents for 2030, 2060, and 2090,to the results obtained by the locational analysis.

5. Results and discussion

5.1. Locational analysis

The locational analysis results defined regions which were gla-cierized, or recently deglacierized, located near LCPs, in areas withless than 40� slope, and where ice thickness is at a minimum. Theconsideration of currently glacierized or recently deglacierizedpasses is important when dealing with glacial archaeological re-mains, as previously suggested. The calculation of LCPs enabled abetter understanding about how people might have travelled fromone location to another based on the principles of walking acrossdifferent landscapes. Although prehistoric and historic landscapesare often uncertain, paleoecological research gives a good generalunderstanding about past landscapes (Berthel et al., 2012; Tinnerand Theurillat, 2003; Wick and Tinner, 1997). The final product ofthe locational analysis displays the overlapping areas of theweighted distance from LCPs, slope, and ice thickness layers in therange of 5 (high potential) to 1 (low potential) for the region(Fig. 3a). The results of locational analysis alone reduced a region ofover 4500 km2 to a 114 km2 area of interest, 8.16 km2 of which isconsidered as high potential (Table 3). Similar to the work con-ducted by Dixon et al. (2005) and Andrews et al. (2012), locationalanalysis provided a means to define small, manageable regions forglacial archaeological prospection. The areas are more finelydelimited in the final GlaciArch model (Section 5.3).

5.2. Glaciological modelling

Future glacier extents for 10-year increments between 2020 and2100 in Switzerland and in Italy were calculated (shown for 2030,2060, and 2090 in Fig. 3b). This regional scale modelling methodprovided a high resolution projection of future glacier extents. Thetotal area of glaciers in 2010 in the Pennine Alps was 446 km2 andcalculated to decrease in future years. For example, a reduction of37%e280 km2 in 2030, 80% to 91 km2 in 2060, and 93% to 30 km2 in2090 was modelled based on the climate scenario used. In thisstudy we did not assess the impact of different assumptions onfuture climate evolution and other glaciological model un-certainties on the results. However, Addor et al. (in press) showedthat the CO2-emission storyline until 2100 only had a small effecton calculated total glacier area.

5.3. GlaciArch

The results of the GlaciArch model show current and futureareas of archaeological potential spread over the Pennine Alps re-gion (see supplementary maps covering the entire region). Asmentioned in Section 5.2, the locational analysis results provided abroadly defined research area. The addition of glaciologicalmodelling results allows the delineations to be further defined. Forexample, between 2010 and 2030, the total area of interest is30.1 km2 and the high potential region is 3.24 km2, compared to thedecreased areas between 2060 and 2090 with 13.7 km2 and

Fig. 3. Results from (a) locational analysis and (b) glaciological modelling from the whole study area. The Swiss Glacier Inventory for 2010 is shown along with the modelled glacierextents for 2010 on the Italian side of the border. The projected glacier extents for 2030, 2060, and 2090 are shown for both sides of the border of the Pennine Alps. The boundariesfor Figs. 1 and 2 are shown in red. Boundaries S1eS5 refer to additional maps not discussed in the text which can be found in the supplementary data section. (For interpretation ofthe references to colour in this figure legend, the reader is referred to the web version of this article.)

S.R. Rogers et al. / Journal of Archaeological Science 52 (2014) 410e420416

0.58 km2, for area of interest and high potential, respectively (seeTable 3 for all values).

Taking a closer look at the results, the area surrounding theMont Collon (Fig. 4) is an area of interest due to its archaeologicalsignificance in the surrounding regions, although no glacialarchaeological finds have been retrieved from that site to date(Fig. 4). In 1948, a Neolithic tool made of flint was located on thePlans de Bertol, which is located on theway to the Col Collon, whichleads archaeologists to believe that it was a pass which was used byhumans for thousands of years (Bezinge and Curdy, 1994, 1995;Sauter, 1950) (Fig. 4). Currently, the model indicates that there is

high glacial archaeological potential on the margins of the HautGlacier d'Arolla and the Glacier du Mont Collon, as well as the areabetween the Petit Mont Collon and the northern section of theGlacier d'Otemma, just south of the Col de Charmontagne (Fig. 4).For the future, the results from GlaciArch show that between 2010and 2030, there will be high potential areas on the margins of thetongue of the Glacier du Mont Collon, as well as the Haut Glacierd’Arolla. Between 2030 and 2060, the Col Collon (5) is expected tobecome an area of high archaeological potential as well as someregions surrounding the Petit Mont Collon. Sections in the middleof the Glacier du Mont Collon and to the north of the southern

Table 3Locational analysis areas calculated for 2010 and the time periods between2010e2030, 2030e2060, and 2060e2090. The high glacial archaeological potential(value 5) areas are also calculated for each time period.

Year(s) Total area (km2) High potential (km2)

2010 114 8.162010e2030 30.1 3.242030e2060 40.5 2.312060e2090 13.7 0.58

S.R. Rogers et al. / Journal of Archaeological Science 52 (2014) 410e420 417

section of the Glacier d'Otemma also show high potential. Ac-cording to the results, it appears that by 2060, the Haut Glacierd'Arolla will have almost completely disappeared. By 2090, verylittle ice will remain, however, the section to the north of the Col duPetit Mont Collon (4) could be of interest at that time.

Already briefly discussed in Section 3, the area surrounding theTheodulpass will now be revisited. The high altitude passes of in-terest near the Theodulpass resulting from this analysis are theBreuiljoch (9), Theodulpass (10), Passo di Ventina Nord (11), and theBreithornpass (12) (Fig. 5). From 2010 to 2030 there are regions ofhigh archaeological potential on the extents of the Furgg, ObererTheodul, and Valtournanche glaciers, as well as on the Theodulpassand Passo de Ventina Nord. Between 2030 and 2060, the east side ofthe Oberer Theodul glacier becomes a predominant area of interest,

Fig. 4. GlaciArch results around the Mont Collon which includes the Col de Petit MontCollon (4) and the Col Collon (5). The Glacier du Mont Collon, Glacier d'Otemma, andHaut glacier d'Arolla are labelled as well as locations mentioned in the text: Petit MontCollon, Col de Charmontagne, and Plans de Bertol. LCP refers to least cost path.

while the Furgg glacier has decreased archaeological potential. Thearea south of the Passo de Ventina also shows high potential duringthat time period. From 2060 to 2090 The Furgg and Oberer Theodulglaciers are predicted to almost completely disappear thereforetheir glacial archaeological potential decreases significantly.

The performance of this model has yet to be tested in the field,however the results seem to correspond well to glaciologicalprinciples and the few glacial archaeological finds already locatedin the region, for example at the Oberer Theodul site (Figs. 4 and 5).In theory, high archaeological potential is expected near the glaciermargins as those are the areas with the thinnest ice, while areas oflow potential should occur mid-glacier where the ice is thickest. Anexample of this can be seen in Fig. 4 at the Haut Glacier d'Arolla;high potential exists on the extents of the glacier tongue and po-tential decreases as inward movement onto the glacier continues.One problem with this model is that it is difficult to conveyinherently dynamic movement on a static map. In fact, potential

Fig. 5. GlaciArch results around the Theodulhorn. Passes listed are the Breuiljoch (9),Theodulpass (10), and the Passo di Ventina Nord (11). The Furgg glacier, and Obererand Unterer Theodul glaciers are labelled. LCP refers to least cost path.

S.R. Rogers et al. / Journal of Archaeological Science 52 (2014) 410e420418

maps should be calculated for each year to obtain a greater un-derstanding about the true and temporally varying archaeologicalpotential of the region, however 2D mapping contraints do notpermit dynamic visualisation of this type of results. An interactiveinterface would be the best way to visualize the end results.

6. Conclusion

In this paper, the new integrated model GlaciArch was used toidentify areas of current and future archaeological interest andpotential in the Pennine Alps. The model highlights areas whichcorrespond to the retrieval of archaeological remains based onglaciological principles and the topographic properties of theterrain. Thus, the GlaciArch results can be used as a decision sup-port tool for the selection of glacial archaeological prospectionsites. The definition of small regions of high glacial archaeologicalpotential means less time, effort, andmoney spent in the field or onflight reconnaissance missions. By combining archaeological andglaciological methods for the first time, a new perspective has beengiven to the field of glacial archaeology. The integration of loca-tional analysis and regional scale glacier modelling proved to bebeneficial for narrowing down a large, often inaccessible, andremote study region to identify zones of archaeological interestbased on glaciological characteristics and human accessibility. Theglacier modelling results forecast a 93% loss in area by 2090. Withthese alarming melting rates, immediate focus should be given tohigh archaeological potential areas in hopes to locate and recoverpossibly irreplaceable, culturally significant items. In order to pro-tect and conserve these exceptional and rare relics, further multi-disciplinary predictive methods should be developed andemployed in sensitive areas such as the Pennine Alps.

Acknowledgements

This research has been supported by the Swiss National ScienceFoundation (grant CR21I2 130279), the Service des bâtiments,monuments et arch�eologie, (Canton du Valais, Switzerland) and theHistory Museum of Valais (Switzerland). Thanks to Philippe Curdyand Sophie Providoli from the History Museum of Valais for theirarchaeological inputs, and Martin Hoelzle for his glaciological dis-cussions. Also, thanks to the three anonymous reviewers whohelped strengthen this paper.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jas.2014.09.010.

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Combining glaciological and archaeological methods for gauging glacial archaeological potential1. Introduction2. Study area and data2.1. Study area2.2. Data

3. Background3.1. Archaeological predictive modelling3.2. Least cost path analysis3.3. Locational analysis3.4. Glacier retreat modelling

4. Methods4.1. High altitude pass selection and LCPs4.2. Locational analysis4.3. Glaciological modelling4.4. GlaciArch

5. Results and discussion5.1. Locational analysis5.2. Glaciological modelling5.3. GlaciArch

6. ConclusionAcknowledgementsAppendix A. Supplementary dataReferences